Learning with Feature Description Logics

We present a paradigm for efficient learning and inference with relational data using propositional means. The paradigm utilizes description logics and concepts graphs in the service of learning relational models using efficient propositional learning algorithms. We introduce a Feature Description Logic (FDL) - a relational (frame based) language that supports efficient inference, along with a generation function that uses inference with descriptions in the FDL to produce features suitable for use by learning algorithms. These are used within a learning framework that is shown to learn efficiently and accurately relational representations in terms of the FDL descriptions. The paradig